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AI Infrastructure Engineer

NMK GLOBAL, INC


Job Location:

San Jose, CA - USA

Monthly Salary: Not provided by the employer
Posted: 3 June 2026 (30+ days ago)
Application Deadline: 31 August 2026
Vacancies: 1 Vacancy
The job posting is outdated and position may be filled

Job Summary

Job Title: AI Infrastructure Engineer (Nutanix AI Platform)

Location: San Jose CA (Hybrid/Onsite Preferred) Duration: 12 months Contract Experience: 10 Years
Rate:
Negotiable Position Overview

We are seeking a highly experienced AI Infrastructure Engineer to architect deploy and optimize enterprise-scale AI infrastructure solutions leveraging the Nutanix ecosystem. The ideal candidate will have deep expertise in Nutanix Cloud Infrastructure (NCI) AOS/AHV Nutanix Kubernetes Platform (NKP) GPU-accelerated computing and hybrid cloud environments.

This role focuses on building scalable high-performance infrastructure that supports Large Language Models (LLMs) Generative AI workloads AI training and AI inference platforms across on-premises and cloud environments.

The selected candidate will serve as a Subject Matter Expert (SME) for Nutanix AI infrastructure and will work closely with architecture cloud platform networking and security teams.

Required Skills (Must Have) Nutanix Platform Expertise
  • Strong hands-on experience with:

    • Nutanix Cloud Infrastructure (NCI)

    • Nutanix AOS (Acropolis Operating System)

    • Nutanix AHV (Acropolis Hypervisor)

    • Nutanix Cloud Manager (NCM)

    • Nutanix Flow

    • Nutanix Objects and Files

Kubernetes & Container Platforms
  • Extensive experience with:

    • Nutanix Kubernetes Platform (NKP)

    • Kubernetes cluster deployment and administration

    • Container orchestration and workload management

    • AI/ML workload deployment in Kubernetes environments

GPU & AI Infrastructure
  • Experience designing and managing GPU-enabled environments

  • Hands-on experience with:

    • NVIDIA GPU ecosystem (A100 H100 CUDA GPU Passthrough vGPU)

    • AMD GPU ecosystem

  • Experience supporting AI model training and inference workloads

Infrastructure Automation
  • Terraform

  • Infrastructure as Code (IaC)

  • Nutanix Calm

  • Automated provisioning and lifecycle management

Monitoring & Observability
  • Prometheus

  • Grafana

  • ELK Stack

  • OpenTelemetry

  • Monitoring logging alerting and performance tuning

Key Responsibilities AI Infrastructure Architecture
  • Design and implement scalable AI infrastructure platforms using Nutanix technologies.

  • Build optimized environments supporting Generative AI LLM training and inference workloads.

  • Design high-performance compute storage and networking architectures for AI applications.

Hybrid Cloud & Multicloud Solutions
  • Architect hybrid cloud solutions leveraging Nutanix Cloud Clusters (NC2).

  • Enable seamless workload portability between on-premises environments and public cloud platforms.

  • Support cloud bursting and dynamic workload scaling.

Kubernetes Platform Engineering
  • Deploy manage and optimize AI workloads on Nutanix Kubernetes Platform (NKP).

  • Design highly available and resilient containerized environments.

  • Implement workload automation and orchestration best practices.

Storage & Data Services
  • Design high-performance storage solutions using Nutanix Objects and Nutanix Files.

  • Optimize storage architectures for AI/ML datasets and model repositories.

  • Ensure data availability scalability and performance.

Security & Networking
  • Implement Zero-Trust security principles.

  • Utilize Nutanix Flow for micro-segmentation and workload security.

  • Collaborate with security and networking teams to protect sensitive AI data.

Performance Optimization
  • Optimize GPU utilization and AI infrastructure performance.

  • Configure GPU Passthrough and vGPU environments.

  • Improve resource efficiency scalability and operational costs.

Observability & Reliability
  • Establish enterprise monitoring logging and alerting frameworks.

  • Ensure high availability disaster recovery and fault tolerance.

  • Perform root cause analysis and capacity planning.

Preferred Qualifications
  • Experience supporting AI/ML platforms LLMs and Generative AI initiatives.

  • Experience with AI model serving frameworks and inference platforms.

  • Knowledge of MLOps AI platform engineering and data pipelines.

  • Experience working in enterprise-scale hybrid cloud environments.

  • Nutanix certifications highly preferred.

Required Experience
  • 10 years of Infrastructure Cloud Platform Engineering or Architecture experience.

  • Strong enterprise-level Nutanix experience.

  • Experience supporting Kubernetes-based production environments.

  • Experience with GPU-enabled infrastructure and AI workloads.